MACHINE LEARNING BASED GCN-LSTM MODEL FOR CROP YIELD PREDICTION USING SPATIAL-TEMPORAL FEATURE LEARNING

Prior research has identified limited data and minimal use of soil characteristics as significant shortcomings. To address these issues, this article conducted extensive data collection for bajra yield prediction and introduces a novel Graph convolution neural network and long short-term memory (GCN-LSTM) model, which consists of three main stages: data collection and processing; spatial feature learning; and prediction. The model breaks the limitations of the traditional methods by using data analytics based on IT and advanced deep learning, making more accurate predictions that can be used in smart agriculture, resource optimization, and food security. Unlike previously studied, deep learning models such as recurrent neural networks (RNN), Long Short-Term Memory (LSTM), and Convolutional neural networks (CNN), the proposed GCN-LSTM model does not assume independence among the districts in the crop yield prediction (CYP) data. Instead, it processes attributes related to crop yield prediction such as meteorological, soil, and climate data. The spatial feature learning is not neglected and is leveraged by the LSTM model for the temporal prediction of crop yield. The performance of the GCN-LSTM model is evaluated on RMSE, R², and correlational coefficients. The experimental results demonstrate that the proposed model significantly outperforms conventional models by effectively incorporating spatial information.